Feature-importance-aware transmission control in semantic communications

Semantic communication shifts the focus from bit-level accuracy to task-relevant meaning. However, most methods assume equal importance across semantic units and rely on costly retraining, limiting scalability. This work proposes an importance-aware framework that accounts for unequal feature contributions. It introduces a new metric, importance-weighted semantic spectral efficiency (wSSE), to prioritize task-relevant features. It develops an empirically derived feature-accuracy matrix, inspired by saturation behavior. The framework enables importance-aware feature selection and subchannel allocation without online learning. This framework targets resource-constrained edge systems such as IoT cameras, UAV detection, and AR/VR. Experiments show up to 73.3% higher efficiency under constrained resources.